Overview

Dataset statistics

Number of variables11
Number of observations1311
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory155.2 KiB
Average record size in memory121.2 B

Variable types

Numeric10
Categorical1

Alerts

External Temperature is highly overall correlated with Feels Like and 2 other fieldsHigh correlation
Feels Like is highly overall correlated with External Temperature and 2 other fieldsHigh correlation
External Humidity is highly overall correlated with External Temperature and 1 other fieldsHigh correlation
Dew Point is highly overall correlated with External Temperature and 1 other fieldsHigh correlation
Clouds is highly overall correlated with DescriptionHigh correlation
Description is highly overall correlated with CloudsHigh correlation
Wind Speed has 280 (21.4%) zerosZeros

Reproduction

Analysis started2023-06-06 11:29:55.461826
Analysis finished2023-06-06 11:31:52.358063
Duration1 minute and 56.9 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

External Temperature
Real number (ℝ)

Distinct661
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.776865
Minimum18.77
Maximum30.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-06-06T17:01:52.460024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum18.77
5-th percentile20.885
Q121.98
median23.18
Q325.26
95-th percentile28.445
Maximum30.07
Range11.3
Interquartile range (IQR)3.28

Descriptive statistics

Standard deviation2.335327
Coefficient of variation (CV)0.098218456
Kurtosis-0.3242438
Mean23.776865
Median Absolute Deviation (MAD)1.45
Skewness0.6794851
Sum31171.47
Variance5.4537521
MonotonicityNot monotonic
2023-06-06T17:01:52.588585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.71 9
 
0.7%
22.27 7
 
0.5%
21.96 7
 
0.5%
21.65 7
 
0.5%
22.39 7
 
0.5%
21.71 7
 
0.5%
22.32 6
 
0.5%
21.66 6
 
0.5%
21.97 6
 
0.5%
21.82 6
 
0.5%
Other values (651) 1243
94.8%
ValueCountFrequency (%)
18.77 1
0.1%
19.11 1
0.1%
19.23 2
0.2%
19.25 1
0.1%
19.27 1
0.1%
19.29 1
0.1%
19.31 1
0.1%
19.32 1
0.1%
19.35 1
0.1%
19.39 1
0.1%
ValueCountFrequency (%)
30.07 1
0.1%
30.01 1
0.1%
29.85 1
0.1%
29.8 1
0.1%
29.59 1
0.1%
29.53 1
0.1%
29.49 1
0.1%
29.47 1
0.1%
29.46 1
0.1%
29.4 1
0.1%

Feels Like
Real number (ℝ)

Distinct651
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.825576
Minimum19.24
Maximum36.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-06-06T17:01:52.732486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum19.24
5-th percentile21.54
Q122.73
median23.99
Q325.98
95-th percentile31.725
Maximum36.23
Range16.99
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation3.0385902
Coefficient of variation (CV)0.12239757
Kurtosis1.2065611
Mean24.825576
Median Absolute Deviation (MAD)1.52
Skewness1.2854552
Sum32546.33
Variance9.2330302
MonotonicityNot monotonic
2023-06-06T17:01:52.866742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.44 10
 
0.8%
22.45 8
 
0.6%
23.57 8
 
0.6%
23.62 8
 
0.6%
22.05 7
 
0.5%
22.69 7
 
0.5%
22.33 7
 
0.5%
23.72 6
 
0.5%
22.46 6
 
0.5%
22.7 6
 
0.5%
Other values (641) 1238
94.4%
ValueCountFrequency (%)
19.24 1
0.1%
19.58 1
0.1%
19.68 1
0.1%
19.69 1
0.1%
19.72 1
0.1%
19.78 1
0.1%
19.79 2
0.2%
19.8 2
0.2%
19.92 1
0.1%
19.97 2
0.2%
ValueCountFrequency (%)
36.23 1
0.1%
35.44 1
0.1%
34.92 1
0.1%
34.76 1
0.1%
34.68 1
0.1%
34.48 1
0.1%
34.4 1
0.1%
34.29 1
0.1%
33.91 1
0.1%
33.85 1
0.1%

Pressure
Real number (ℝ)

Distinct12
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1011.0359
Minimum1004
Maximum1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-06-06T17:01:52.986799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1004
5-th percentile1008
Q11010
median1011
Q31012
95-th percentile1014
Maximum1015
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9064605
Coefficient of variation (CV)0.0018856507
Kurtosis-0.15514315
Mean1011.0359
Median Absolute Deviation (MAD)1
Skewness-0.35915252
Sum1325468
Variance3.6345916
MonotonicityNot monotonic
2023-06-06T17:01:53.222062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1011 267
20.4%
1012 253
19.3%
1010 201
15.3%
1013 189
14.4%
1009 153
11.7%
1014 104
 
7.9%
1008 74
 
5.6%
1007 35
 
2.7%
1015 17
 
1.3%
1006 14
 
1.1%
Other values (2) 4
 
0.3%
ValueCountFrequency (%)
1004 1
 
0.1%
1005 3
 
0.2%
1006 14
 
1.1%
1007 35
 
2.7%
1008 74
 
5.6%
1009 153
11.7%
1010 201
15.3%
1011 267
20.4%
1012 253
19.3%
1013 189
14.4%
ValueCountFrequency (%)
1015 17
 
1.3%
1014 104
 
7.9%
1013 189
14.4%
1012 253
19.3%
1011 267
20.4%
1010 201
15.3%
1009 153
11.7%
1008 74
 
5.6%
1007 35
 
2.7%
1006 14
 
1.1%

External Humidity
Real number (ℝ)

Distinct47
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.610221
Minimum46
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-06-06T17:01:53.350862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile68
Q184
median92
Q396
95-th percentile99
Maximum100
Range54
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.7714268
Coefficient of variation (CV)0.11027426
Kurtosis0.83399647
Mean88.610221
Median Absolute Deviation (MAD)5
Skewness-1.2181018
Sum116168
Variance95.480781
MonotonicityNot monotonic
2023-06-06T17:01:53.478314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
96 135
 
10.3%
97 102
 
7.8%
98 93
 
7.1%
94 79
 
6.0%
95 68
 
5.2%
93 67
 
5.1%
99 65
 
5.0%
92 62
 
4.7%
91 57
 
4.3%
90 51
 
3.9%
Other values (37) 532
40.6%
ValueCountFrequency (%)
46 1
 
0.1%
50 1
 
0.1%
53 1
 
0.1%
55 1
 
0.1%
58 4
0.3%
59 1
 
0.1%
60 3
0.2%
61 5
0.4%
62 4
0.3%
63 5
0.4%
ValueCountFrequency (%)
100 10
 
0.8%
99 65
5.0%
98 93
7.1%
97 102
7.8%
96 135
10.3%
95 68
5.2%
94 79
6.0%
93 67
5.1%
92 62
4.7%
91 57
4.3%

Dew Point
Real number (ℝ)

Distinct463
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.656857
Minimum11.13
Maximum25.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-06-06T17:01:53.615558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum11.13
5-th percentile19.56
Q120.975
median21.65
Q322.44
95-th percentile23.655
Maximum25.52
Range14.39
Interquartile range (IQR)1.465

Descriptive statistics

Standard deviation1.2450992
Coefficient of variation (CV)0.057492144
Kurtosis4.2905537
Mean21.656857
Median Absolute Deviation (MAD)0.73
Skewness-0.64118441
Sum28392.14
Variance1.5502719
MonotonicityNot monotonic
2023-06-06T17:01:53.764241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.09 11
 
0.8%
21.42 10
 
0.8%
22.36 10
 
0.8%
21.04 9
 
0.7%
21.13 9
 
0.7%
22.77 8
 
0.6%
22.23 8
 
0.6%
22.16 8
 
0.6%
21.59 8
 
0.6%
21.33 8
 
0.6%
Other values (453) 1222
93.2%
ValueCountFrequency (%)
11.13 1
0.1%
15.84 1
0.1%
16.92 1
0.1%
17.61 1
0.1%
17.96 1
0.1%
18.1 1
0.1%
18.26 1
0.1%
18.28 1
0.1%
18.34 1
0.1%
18.36 2
0.2%
ValueCountFrequency (%)
25.52 1
0.1%
25.37 1
0.1%
25.21 1
0.1%
24.92 1
0.1%
24.91 2
0.2%
24.83 1
0.1%
24.8 1
0.1%
24.76 2
0.2%
24.7 1
0.1%
24.61 1
0.1%

Clouds
Real number (ℝ)

Distinct99
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.249428
Minimum0
Maximum100
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-06-06T17:01:53.982355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q140
median87
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)60

Descriptive statistics

Standard deviation29.333453
Coefficient of variation (CV)0.40045983
Kurtosis-1.0284355
Mean73.249428
Median Absolute Deviation (MAD)13
Skewness-0.68172118
Sum96030
Variance860.45148
MonotonicityNot monotonic
2023-06-06T17:01:54.157653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 371
28.3%
40 175
 
13.3%
99 69
 
5.3%
75 65
 
5.0%
20 52
 
4.0%
98 30
 
2.3%
97 28
 
2.1%
94 27
 
2.1%
96 25
 
1.9%
95 22
 
1.7%
Other values (89) 447
34.1%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
3 1
0.1%
4 1
0.1%
5 2
0.2%
6 2
0.2%
7 2
0.2%
8 2
0.2%
9 2
0.2%
10 2
0.2%
ValueCountFrequency (%)
100 371
28.3%
99 69
 
5.3%
98 30
 
2.3%
97 28
 
2.1%
96 25
 
1.9%
95 22
 
1.7%
94 27
 
2.1%
93 18
 
1.4%
92 15
 
1.1%
91 12
 
0.9%

Wind Speed
Real number (ℝ)

Distinct290
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.084958
Minimum0
Maximum6.7
Zeros280
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-06-06T17:01:54.293143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.375
median1
Q31.57
95-th percentile2.78
Maximum6.7
Range6.7
Interquartile range (IQR)1.195

Descriptive statistics

Standard deviation0.91807739
Coefficient of variation (CV)0.84618699
Kurtosis1.9526998
Mean1.084958
Median Absolute Deviation (MAD)0.59
Skewness1.0333355
Sum1422.38
Variance0.84286609
MonotonicityNot monotonic
2023-06-06T17:01:54.441525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 280
 
21.4%
0.5 65
 
5.0%
1 53
 
4.0%
1.5 25
 
1.9%
2.1 14
 
1.1%
1.35 11
 
0.8%
1.59 10
 
0.8%
1.44 9
 
0.7%
1.51 9
 
0.7%
1.48 9
 
0.7%
Other values (280) 826
63.0%
ValueCountFrequency (%)
0 280
21.4%
0.02 1
 
0.1%
0.07 1
 
0.1%
0.11 1
 
0.1%
0.13 2
 
0.2%
0.14 1
 
0.1%
0.15 2
 
0.2%
0.16 2
 
0.2%
0.19 3
 
0.2%
0.2 1
 
0.1%
ValueCountFrequency (%)
6.7 1
0.1%
5.1 1
0.1%
4.92 1
0.1%
4.77 1
0.1%
4.6 1
0.1%
4.55 1
0.1%
4.49 1
0.1%
4.38 1
0.1%
4.26 1
0.1%
4.22 2
0.2%

Description
Categorical

Distinct14
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size20.5 KiB
light rain
368 
overcast clouds
311 
scattered clouds
175 
broken clouds
156 
moderate rain
98 
Other values (9)
203 

Length

Max length27
Median length23
Mean length13.337147
Min length4

Characters and Unicode

Total characters17485
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowscattered clouds
2nd rowbroken clouds
3rd rowbroken clouds
4th rowscattered clouds
5th rowlight rain

Common Values

ValueCountFrequency (%)
light rain 368
28.1%
overcast clouds 311
23.7%
scattered clouds 175
13.3%
broken clouds 156
11.9%
moderate rain 98
 
7.5%
few clouds 74
 
5.6%
heavy intensity rain 51
 
3.9%
light intensity shower rain 21
 
1.6%
clear sky 16
 
1.2%
mist 13
 
1.0%
Other values (4) 28
 
2.1%

Length

2023-06-06T17:01:54.567729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clouds 716
26.4%
rain 550
20.3%
light 395
14.6%
overcast 311
11.5%
scattered 175
 
6.5%
broken 156
 
5.8%
moderate 98
 
3.6%
intensity 78
 
2.9%
few 74
 
2.7%
heavy 52
 
1.9%
Other values (8) 105
 
3.9%

Most occurring characters

ValueCountFrequency (%)
1399
 
8.0%
t 1376
 
7.9%
r 1376
 
7.9%
s 1351
 
7.7%
o 1323
 
7.6%
e 1282
 
7.3%
c 1218
 
7.0%
a 1202
 
6.9%
l 1133
 
6.5%
i 1131
 
6.5%
Other values (13) 4694
26.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16086
92.0%
Space Separator 1399
 
8.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1376
 
8.6%
r 1376
 
8.6%
s 1351
 
8.4%
o 1323
 
8.2%
e 1282
 
8.0%
c 1218
 
7.6%
a 1202
 
7.5%
l 1133
 
7.0%
i 1131
 
7.0%
d 1016
 
6.3%
Other values (12) 3678
22.9%
Space Separator
ValueCountFrequency (%)
1399
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16086
92.0%
Common 1399
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1376
 
8.6%
r 1376
 
8.6%
s 1351
 
8.4%
o 1323
 
8.2%
e 1282
 
8.0%
c 1218
 
7.6%
a 1202
 
7.5%
l 1133
 
7.0%
i 1131
 
7.0%
d 1016
 
6.3%
Other values (12) 3678
22.9%
Common
ValueCountFrequency (%)
1399
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1399
 
8.0%
t 1376
 
7.9%
r 1376
 
7.9%
s 1351
 
7.7%
o 1323
 
7.6%
e 1282
 
7.3%
c 1218
 
7.0%
a 1202
 
6.9%
l 1133
 
6.5%
i 1131
 
6.5%
Other values (13) 4694
26.8%

average_internal_temp
Real number (ℝ)

Distinct608
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.679634
Minimum17.5
Maximum39.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-06-06T17:01:54.668845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum17.5
5-th percentile20.341667
Q122.1
median23.633333
Q329.325
95-th percentile34.533333
Maximum39.6
Range22.1
Interquartile range (IQR)7.225

Descriptive statistics

Standard deviation4.6999557
Coefficient of variation (CV)0.18302269
Kurtosis-0.55377658
Mean25.679634
Median Absolute Deviation (MAD)2.2333333
Skewness0.81307599
Sum33666
Variance22.089584
MonotonicityNot monotonic
2023-06-06T17:01:54.791215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 12
 
0.9%
22.9 12
 
0.9%
24 11
 
0.8%
21.6 10
 
0.8%
22.1 10
 
0.8%
22.25 9
 
0.7%
21.7 9
 
0.7%
21.8 9
 
0.7%
21.9 9
 
0.7%
22.75 8
 
0.6%
Other values (598) 1212
92.4%
ValueCountFrequency (%)
17.5 1
0.1%
18.5 1
0.1%
18.9 1
0.1%
19 1
0.1%
19.05 1
0.1%
19.1 1
0.1%
19.13333333 1
0.1%
19.2 1
0.1%
19.25 2
0.2%
19.26666667 2
0.2%
ValueCountFrequency (%)
39.6 1
0.1%
38.73333333 1
0.1%
38.7 1
0.1%
37.7 2
0.2%
37.65 1
0.1%
37.46666667 1
0.1%
37.46666667 1
0.1%
37.35 1
0.1%
37.3 1
0.1%
37.15 1
0.1%

average_internal_humidity
Real number (ℝ)

Distinct373
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.935011
Minimum36.5
Maximum96.633333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-06-06T17:01:54.934921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum36.5
5-th percentile58.183333
Q190.716667
median95
Q395.966667
95-th percentile96.633333
Maximum96.633333
Range60.133333
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation12.589976
Coefficient of variation (CV)0.14156378
Kurtosis2.3975808
Mean88.935011
Median Absolute Deviation (MAD)1.5
Skewness-1.8900782
Sum116593.8
Variance158.5075
MonotonicityNot monotonic
2023-06-06T17:01:55.056442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 290
22.1%
96.63333333 219
16.7%
94.5 111
 
8.5%
96.3 86
 
6.6%
93.5 48
 
3.7%
93 39
 
3.0%
95.63333333 38
 
2.9%
95.3 33
 
2.5%
94 15
 
1.1%
95.96666667 10
 
0.8%
Other values (363) 422
32.2%
ValueCountFrequency (%)
36.5 1
0.1%
41.63333333 1
0.1%
42 1
0.1%
43.5 1
0.1%
43.73333333 1
0.1%
43.9 1
0.1%
43.93333333 1
0.1%
46.63333333 1
0.1%
46.7 1
0.1%
47.76666667 1
0.1%
ValueCountFrequency (%)
96.63333333 219
16.7%
96.56666667 1
 
0.1%
96.53333333 1
 
0.1%
96.5 1
 
0.1%
96.43333333 1
 
0.1%
96.36666667 1
 
0.1%
96.3 86
 
6.6%
96.26666667 1
 
0.1%
96.23333333 1
 
0.1%
96.2 1
 
0.1%

light
Real number (ℝ)

Distinct615
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3267.7892
Minimum-2
Maximum27306.2
Zeros6
Zeros (%)0.5%
Negative116
Negative (%)8.8%
Memory size20.5 KiB
2023-06-06T17:01:55.188774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q11.042
median1.042
Q34765.625
95-th percentile15742.7
Maximum27306.2
Range27308.2
Interquartile range (IQR)4764.583

Descriptive statistics

Standard deviation5721.6838
Coefficient of variation (CV)1.7509342
Kurtosis4.4864218
Mean3267.7892
Median Absolute Deviation (MAD)3.042
Skewness2.1586129
Sum4284071.7
Variance32737665
MonotonicityNot monotonic
2023-06-06T17:01:55.314433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.042 536
40.9%
-2 63
 
4.8%
-1 53
 
4.0%
27306.2 8
 
0.6%
2.5 6
 
0.5%
0 6
 
0.5%
2.917 5
 
0.4%
6.458 5
 
0.4%
3.125 4
 
0.3%
1.25 4
 
0.3%
Other values (605) 621
47.4%
ValueCountFrequency (%)
-2 63
 
4.8%
-1 53
 
4.0%
0 6
 
0.5%
1.042 536
40.9%
1.25 4
 
0.3%
1.458 2
 
0.2%
1.875 4
 
0.3%
2.083 1
 
0.1%
2.292 1
 
0.1%
2.5 6
 
0.5%
ValueCountFrequency (%)
27306.2 8
0.6%
26764.2 1
 
0.1%
26467.1 1
 
0.1%
26404.6 1
 
0.1%
25959.2 1
 
0.1%
25729.6 1
 
0.1%
25510.8 1
 
0.1%
25354.2 1
 
0.1%
25136.2 1
 
0.1%
24795 1
 
0.1%

Interactions

2023-06-06T17:01:18.157807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T16:59:56.734109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:07.834898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:16.678361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:25.456228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:33.731167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:42.261161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:50.981739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:01:01.507637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:01:10.594537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:01:20.900020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T16:59:57.025380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-06-06T17:00:16.816489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-06-06T17:01:02.195619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-06-06T17:00:26.680199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:35.252016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:43.437629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:52.326970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:01:03.122216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:01:11.736597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:01:43.492529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T16:59:59.176261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:09.234302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:18.154647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:26.800318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:35.404846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:43.583795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:00:52.480122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:01:03.370399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-06T17:01:11.882890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-06-06T17:01:55.542991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
External TemperatureFeels LikePressureExternal HumidityDew PointCloudsWind Speedaverage_internal_tempaverage_internal_humiditylightDescription
External Temperature1.0000.997-0.173-0.8150.585-0.1530.2190.256-0.3390.2640.108
Feels Like0.9971.000-0.173-0.7850.624-0.1380.2150.261-0.3290.2540.106
Pressure-0.173-0.1731.0000.144-0.030-0.010-0.016-0.2970.178-0.2710.126
External Humidity-0.815-0.7850.1441.000-0.1080.288-0.264-0.1030.366-0.2700.070
Dew Point0.5850.624-0.030-0.1081.0000.1570.0740.298-0.1060.0550.117
Clouds-0.153-0.138-0.0100.2880.1571.0000.1660.0670.1420.0250.575
Wind Speed0.2190.215-0.016-0.2640.0740.1661.000-0.017-0.0450.0870.098
average_internal_temp0.2560.261-0.297-0.1030.2980.067-0.0171.000-0.4850.4520.092
average_internal_humidity-0.339-0.3290.1780.366-0.1060.142-0.045-0.4851.000-0.2720.087
light0.2640.254-0.271-0.2700.0550.0250.0870.452-0.2721.0000.257
Description0.1080.1060.1260.0700.1170.5750.0980.0920.0870.2571.000

Missing values

2023-06-06T17:01:52.103757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-06T17:01:52.266930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

External TemperatureFeels LikePressureExternal HumidityDew PointCloudsWind SpeedDescriptionaverage_internal_tempaverage_internal_humiditylight
datetime
2020-10-22 00:00:0022.6923.2110098419.85400.00scattered clouds23.43333396.3000001.042
2020-10-22 01:00:0022.7123.4610119321.52702.34broken clouds23.30000096.3666671.042
2020-10-22 02:00:0025.0625.8910128722.74692.51broken clouds23.00000095.0000001.042
2020-10-22 03:00:0025.6626.2410117520.90401.50scattered clouds22.60000096.6333331.042
2020-10-22 04:00:0025.8326.7710128823.69654.38light rain22.90000095.0000001.042
2020-10-22 05:00:0027.5231.3110128324.37484.55light rain22.70000096.2000001.042
2020-10-22 08:00:0027.7331.8610098324.58294.92light rain29.83333383.1333336999.17
2020-10-22 09:00:0027.8330.9810077522.99406.70scattered clouds31.06666785.96666715679.6
2020-10-22 10:00:0025.9226.7910088523.20494.26light rain31.90000076.5333338890.42
2020-10-22 11:00:0025.4926.3710088723.17573.64light rain29.83333389.70000014612.1
External TemperatureFeels LikePressureExternal HumidityDew PointCloudsWind SpeedDescriptionaverage_internal_tempaverage_internal_humiditylight
datetime
2020-12-30 14:00:0023.6324.4010109021.89371.24scattered clouds31.10000067.76666713084.2
2020-12-30 15:00:0023.4024.2810109522.55200.00thunderstorm32.35000066.00000012345.4
2020-12-30 16:00:0023.4724.2210129021.73441.48scattered clouds33.36666764.9333337014.58
2020-12-30 17:00:0022.8823.6510119321.69481.53scattered clouds27.83333391.833333543.333
2020-12-30 18:00:0022.7423.5210109421.72400.00scattered clouds25.65000095.00000070
2020-12-30 19:00:0022.8223.6110119421.80521.50broken clouds24.43333396.6333331.042
2020-12-30 20:00:0022.6323.4310109521.79651.46broken clouds24.06666796.6333331.042
2020-12-30 21:00:0022.3623.1610099621.69400.00light rain23.60000096.6333331.042
2020-12-30 22:00:0021.8922.6410099621.22861.56overcast clouds24.25000095.0000001.042
2020-12-30 23:00:0021.8822.6610099721.38921.59overcast clouds24.05000095.0000001.042